Alzheimer's disease (AD) is the most common form of dementia in the United States. Effective treatments for it have remained elusive, and this may be due in part to an incomplete understanding of the biological mechanisms underlying its pathogenesis on the molecular, cellular, and tissue levels. There is data suggesting that myelin and oligodendrocytes may play a role in promoting AD pathology. For example, neuroimaging studies have suggested that white matter lesions may presage Alzheimer's symptom onset by many years. However, there is an incomplete understanding of the underlying molecular mechanisms through which dysregulated myelination and oligodendrocytes may play a role in Alzheimer's disease. In order to address this question, we have previously performed weighted gene co-expression network analysis on gene expression data generated from postmortem brain tissue in Alzheimer's patients and non- demented controls. The subnetworks enriched for myelination functions and oligodendrocyte markers contain many genes that have been related to Alzheimer's through both GWAS and amyloid-related pathophysiology. We also found that gene expression across the myelination subnetworks is highly correlated with quantitative measures of Alzheimer's disease pathology (such as amyloid plaque burden and Braak score), and that there is a large loss of gene-gene correlation within the myelination subnetworks in Alzheimer's disease samples. Next, we identified key regulatory genes in the myelin subnetworks across brain regions, some of which have altered gene expression in Alzheimer's disease themselves. These data take an unbiased approach to suggest that myelination and/or oligodendrocytes may play a crucial role in promoting Alzheimer's disease; however, it is necessary to further test the causality of this claim, as well as to characterize the molecular mechanisms at play that will be necessary to rationally design interventions. The goals of this project are to query the robustness of the Alzheimer's disease risk gene associations, to measure the Alzheimer's disease stage at which myelination subnetworks show the most dysregulation, and to use mouse models to identify downstream changes following perturbations of the key regulator genes that are relevant to Alzheimer's disease. In particular, we propose the following Specific Aims: Aim 1. Develop robust and high-resolution network models of myelination dysregulation in AD using human postmortem brain data. Aim 2. Implicate key drivers of the myelination subnetworks in AD pathways using mice models.